Released
SoftwareThe Smart Monitoring Workflow (Tocap) of the Flood Event Explorer: Determining the most suitable time and location for event-driven, ad-hoc monitoring
Cite as:
Eggert, Daniel; Nixdorf, Erik (2022): The Smart Monitoring Workflow (Tocap) of the Flood Event Explorer: Determining the most suitable time and location for event-driven, ad-hoc monitoring. GFZ Data Services. https://doi.org/10.5880/GFZ.1.4.2022.004Status
I N R E V I E W : Eggert, Daniel; Nixdorf, Erik (2022): The Smart Monitoring Workflow (Tocap) of the Flood Event Explorer: Determining the most suitable time and location for event-driven, ad-hoc monitoring. GFZ Data Services. https://doi.org/10.5880/GFZ.1.4.2022.004
Abstract
The Smart Monitoring Workflow (Tocap) is part of the Flood Event Explorer (FEE, Eggert et al., 2022), developed at the GFZ German Research Centre for Geosciences in close collaboration with the Helmholtz-Centre for Environmental Research UFZ Leipzig. It is funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/). A deeper understanding of the Earth system as a whole and its interacting sub-systems depends not only on accurate mathematical approximations of the physical processes but also on the availability of environmental data across time and spatial scales. Even though advanced numerical simulations and satellite-based remote sensing in conjunction with sophisticated algorithms such as machine learning tools can provide 4D environmental datasets, local and mesoscale measurements continue to be the backbone in many disciplines such as hydrology. Considering the limitations of human and technical resources, monitoring strategies for these types of measurements should be well designed to increase the information gain provided. One helpful set of tools to address these tasks are data exploration frameworks providing qualified data from different sources and tailoring available computational and visual methods to explore and analyse multi-parameter datasets. In this context, we developed a Smart Monitoring Workflow to determine the most suitable time and location for event-driven, ad-hoc monitoring in hydrology using soil moisture measurements as our target variable.
The Smart Monitoring Workflow consists of three main steps. First is the identification of the region of interest, either via user selection or recommendation based on spatial environmental parameters provided by the user. Statistical filters and different color schemes can be applied to highlight different regions. The second step is accessing time-dependent environmental parameters (e.g., rainfall and soil moisture estimates of the recent past, weather predictions from numerical weather models and swath forecasts from Earth observation satellites) for the region of interest and visualizing the results. Lastly, a detailed assessment of the region of interest is conducted by applying filter and weight functions in combination with multiple linear regressions on selected input parameters. Depending on the measurement objective (e.g highest/lowest values, highest/lowest change), most suitable areas for monitoring will subsequently be visually highlighted. In combination with the provided background map, an efficient route for monitoring can be planned directly in the exploration environment.
The added value of the Smart Monitoring Workflow is multifold. The workflow gives the user a set of tools to visualize and process their data on a background map and in combination with data from public environmental datasets. For raster data from public databases, tailor-made routines are provided to access the data in the spatial-temporal limits required by the user. Aiming to facilitate the design of terrestrial monitoring campaigns, the platform and device-independent approach of the workflow gives the user the flexibility to design a campaign at the desktop computer first and to refine it later in the field using mobile devices. In this context, the ability of the workflow to plot time-series of forecast data for the region of interest empowers the user to react quickly to changing conditions, e.g thunderstorm showers, by adapting the monitoring strategy, if necessary. Finally, the integrated routing algorithm assists to calculate the duration of a planned campaign as well as the optimal driving route between often scattered monitoring locations.
Technical Information
Copyright 2022 Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany / DE Flood Event ExplorerLicensed under the Apache License, Version 2.0 (the "License"); you may not use these files except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Authors
- Eggert, Daniel
;GFZ German Research Centre for Geosciences, Potsdam, Germany - Nixdorf, Erik;Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
Contact
- Eggert, Daniel; GFZ German Research Centre for Geosciences; ➦
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